TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.
Learn more
Mirai
Mirai is a developer-focused on-device AI infrastructure platform designed to convert, optimize, and run machine learning models directly on Apple devices with high performance and privacy. It provides a unified pipeline that enables teams to convert and quantize models, benchmark them, distribute them, and execute inference locally. It is built specifically for Apple Silicon and aims to deliver near-zero latency, zero inference cost, and full data privacy by keeping sensitive processing on the user’s device. Through its SDK and inference engine, developers can integrate AI features into applications quickly, using hardware-aware optimizations that unlock the full power of the GPU and Neural Engine. Mirai also includes dynamic routing capabilities that automatically decide whether a request should run locally or in the cloud based on latency, privacy, or workload requirements.
Learn more
Jectora
Jectora is an OpenAI-compatible AI gateway built for organizations. One API (the Jectora API base URL) routes requests across models from multiple providers with smart routing, hardware-aware inference optimization, and intelligent caching for lower cost and latency. Keep the official OpenAI JavaScript or Python SDK: set the Jectora base URL and API key, then choose a model by ID. Chat Completions, Responses, Embeddings, and model discovery are supported; capabilities vary by model. Organization workspaces separate API keys, balance, usage, and billing. Scope each key by model, spend, expiration, or source IP. The Dashboard shows spend, requests, and tokens. Pricing is pay as you go: $0 monthly fee, no minimum spend, prepaid credits with no fixed expiration while active, and no subscription, platform fee, or long-term commitment. Unit prices are in the model catalog (USD per 1M tokens for token-based models). Sign in with GitHub. Docs and support are on the Jectora website.
Learn more
Mu
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries.
Learn more